Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

State-of-the-art federated learning algorithms such as FedAvg require carefully tuned
stepsizes to achieve their best performance. The improvements proposed by existing …

Clustered federated learning (CFL) is a promising solution to address the non-IID problem in
the spatial domain for federated learning (FL). However, existing CFL solutions overlook the …

Modern deep neural networks often require distributed training with many workers due to
their large size. As worker numbers increase, communication overheads become the main …

Federated learning is a paradigm of distributed machine learning in which multiple clients
coordinate with a central server to learn a model, without sharing their own training data …

Federated Learning (FL) is a distributed framework for collaborative model training over
large-scale distributed data. Centralized FL leverages a server to aggregate client models …

The sharpness-aware minimization (SAM) procedure recently gained increasing attention
due to its favorable generalization ability to unseen data. SAM aims to find flatter (local) …